Dynamic contextual video capture

ABSTRACT

Embodiments of the present invention provide a method, computer program product and system for dynamic video capture through a contact lens based on dynamic contextual identification. Initially, a set of identifying information and a video stream from a contact lens is received. A determination is made as to whether to capture the video stream, based on the received information. The determining to capture the video stream is based on, at least one of, the user interest level exceeding a threshold and detecting a contextual identifier within the received the video stream from a contact lens. Responsive to determining to capture the video stream, the video stream is classified into a category and saving based on the classification category of the video stream.

BACKGROUND

The present invention relates generally to the field of video recordingof a target from the perspective of a user, and more particularly tolearning when to commence and end recording based on various scenariosand/or conditions.

In recent years, an increasing number of individuals wear prescriptioncontact lenses in order to correct visual defects thereby improving onesvision. Generally, contact lenses are worn to correct vision and/or forcosmetic reasons, (i.e., alter the appearance of one's eye).

Advances in technology are leading towards the miniaturization ofcommonly used devices. Computing devices, for instance, have benefitedfrom recent advancements in microprocessor design, providingincreasingly complex computations while decreasing the size ofrespective components of the device. For example, the hardware neededfor video recording can me miniaturized and associated with a variety ofdevices such as a camera on a mobile/smart phone, a camera on a smartwatch, etc.

SUMMARY

Another embodiment of the present invention provides a computer programproduct for dynamic contextual video capture through a contact Accordingto an aspect of the present invention, there is a method that performsthe following operations (not necessarily in the following order):receiving, by one or more processors, a set of identifying informationand a video stream from a contact lens; determining, by one or moreprocessors, whether to capture the video stream, wherein determining tocapture the video stream is based on, at least one of, the user interestlevel exceeding a threshold and detecting a contextual identifier withinthe received the video stream from a contact lens; classifying, by oneor more processor, the video stream into a category, and responsive todetermining to capture the video stream, saving, by one or moreprocessors, the video stream based on the classification category of thevideo stream.

Another embodiment of the present invention provides a computer programproduct for dynamic contextual video capture through a contact lens,based on the method described above.

Another embodiment of the present invention provides a computer systemfor dynamic contextual video capture through a contact lens, based onthe method described above.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram of a human eye, in accordance withan embodiment of the present invention;

FIG. 2 is a functional block diagram illustrating a data processingenvironment, in accordance with an embodiment of the present invention;

FIG. 3 is a flow chart illustrating operational steps for contextualvideo capture in accordance with an embodiment of the present invention;and

FIG. 4 is a block diagram of the internal and external components of acomputer system, in accordance with an embodiment of the presentinvention.

DETAILED DESCRIPTION

Electronic devices have become an essential part of daily life. Advancesin electronic technology allow for devices to communicate and exchangedata. Many devices have ‘smartness’ features enabling such devices andsystems to be programmed to operate and communicate electronically overthe internet. Additionally, a ‘smartness’ feature associated with manydevices and systems provides for systems to run independent of directhuman control and/or human supervision through either artificialintelligence and/or machine learning.

Embodiments of the present invention combine miniaturization of commonlyused devices with a contact lens which can learn when to record what thewearer of the contact lens sees. Specifically, embodiments of thepresent invention provide solutions for self-learning lenses that candetermine appropriate instances to record.

Embodiments of the present invention improve determinations made toautomatically start and stop recording. Specifically, embodiments of thepresent invention provide solutions for determining when to and when notto record subject matter utilizing a contact lens with a built in cameraand sensors. This smart contact lens enables the user to discretely weara recording device and upload recorded information as needed to one ormore locations. More specifically, embodiments of the present inventionreveal situations defined and/or learned where a user does not want tovideo record and/or have the ability to classify a video recording asrestricted, public and/or private in a repository along with an abilityto share the recording on social media.

Embodiments of the present invention recognize the need for moreeffective determination of when to automatically commence recordingimages, and when to stop recording images. For example, typically, arecording device has a manual setting and relies on the user to selectwhen to start and stop recording a subject matter and/or event. In someinstances, the user of the recording device may have the capability tosave the captured event privately or make the captured event publiclythrough some form of social media sharing service. Embodiments of thepresent invention provide solutions for automatically commencingrecording of a subject matter and/or event, identifying contextualinformation of the recorded subject matter, and save the recordedsubject matter based on the identified contextual information using oneor more computer processors of a contact lens. Specifically, embodimentsof the present invention classify the content of the image anddetermines whether to record based on defined and learned contextualidentifiers in detected in the image. A contextual identifier representsan identifiable a person, place, item, object, activity and the like,through cognitive analytical systems. A contextual identifier can bepredefined or machine learning. Further, a contextual identifier may beidentified through facial recognition, and object recognition, varioussensors to identify a specific activity (e.g., biking, running, dancing,etc.), location determination systems (e.g., GPS) and the like. Further,embodiments of the present invention classify situations that videorecording should be restricted based on defined and learned contextualidentifiers. Further, embodiments of the present invention can, based onthe situation and contextual identifiers may determine where and how tosave a recorded video. For example, embodiments of the present inventionmay determine to save the video on the cloud, on a personal server, on apersonal computing device, on a personal smart phone, etc. In anotherexample, embodiments of the present invention may determine how to savethe video such that the video may be classified as restricted, privateand/or personal, dependent on the situation and contextual identifiers.

The present invention will now be described in detail with reference tothe Figures. FIG. 1 portrays environment 100, depicting a user's eyerepresented as eye 110 and smart contact lens 120. Elements of eye 110include, but is not limited to iris 112, pupil 114 as well as otherelements not depicted in FIG. 1 (e.g., the cornea, lens, aqueous andvitreous humor). Generally, an eye operates by focusing light from theenvironment onto the retina of the eye, such that images of theenvironment are presented in-focus on the retina. The natural lens ofthe eye can be controlled (e.g., by ciliary muscles of the eye) to allowobjects at different distances to be in focus at different points intime.

Iris 112 is the heavily pigmented portion of a human eye. Generally,iris 112 may be brown, green, or blue depending on the amount anddistribution of pigment. Pupil 114 is a hole within eye 110 in whichlight passes through. Iris 112 and pupil 114 may inversely change sizeto allow for different levels of light to enter eye 110. Specifically,pupil 114 is an opening within iris 112. One or more muscles (e.g., thesphincter muscle, not shown) controls the diameter of pupil 114. Forexample, by relaxing (dilating and enlarging) pupil 114, a greateramount of light can enter eye 110. Alternatively, by constricting(reducing) pupil 114, less light may enter eye 110. Typically, pupil 114diameter changes between 3-7 mm. As pupil 114 diameter changes (i.e.,larger or smaller), so too does iris 112 diameter change in an inverseproportion.

Pupil 114 may constrict or dilate for numerous reasons. For example,when ambient light levels increase, pupil 114 constricts so that lesslight will enter eye 110. Pupil 114 may dilate when ambient light levelsare low, thereby allowing a greater amount of light to enter eye 110. Inaddition to pupil 114 changing its size based on ambient light, pupil114 may constrict or dilate based on the person's emotions and/orfeelings towards the subject the person is looking at. For example, ifthe user is experiencing pain, experiencing one or more intense emotions(e.g., happiness, sadness, anger, surprised, fear, stress, etc.), etc,pupil 114 may constrict or dilate. Similarly, mental activity level mayaffect pupil dilation. For example, level of attention, level ofinterest, cognitive load, etc. may affect pupil dilation. In anotherexample, pupil size may change based on a specific activity, such aslying. Further, pupil 114 may constrict or dilate based on otherinfluences such as medication, alcohol, and certain drugs. Additionally,pupil 114 may constrict or dilate based on medical issues such asneurological disorders and concussions.

Therefore, embodiments of the present invention recognize that pupilsize may be an indicator of a person's interest in a particular subjectand/or state of mind.

Smart contact lens 120 may be worn on one or both eyes of a user. Asdepicted in an exploded view in FIG. 1, smart contact lens is separatedfrom eye 110, for illustration purposes only. However when worn by user,smart contact lens 120 is worn directly over eye 110, similarly to atraditional contact lens used to correct vision. FIG. 1 depicts only asingle smart contact lens 120 and single eye 110, however, it should beunderstood that a smart contact lens 120 may be worn on each eye 110 ofa user.

Generally, smart contact lens 120 is exposed both to air and a body'sinternal chemistry. In this embodiment, smart contact lens 120 is madeto an optimal specification where smart contact lens is strong enough towithstand the mechanical movement of blinking, an impact by a humantouch, being in contact with a foreign object (e.g., dust, hair, andother unknown particulates), yet comfortable enough to be worn adjacenthuman eye 110. A traditional contact lens sits on directly over eye 110to enhance vision. Smart contact lens 120 may also possess visionimprovement characteristics.

Smart contact lens 120 automatically commences recording of a subjectmatter and/or event, identifies contextual information of the recordedsubject matter, and saves the recorded subject matter based on theidentified contextual information using one or more computer processorsof a smart contact lens 120 (not shown in FIG. 1), as described ingreater detail with regard to FIGS. 2 and 3. In this embodiment, smartcontact lens 120 may capture biometric information related to the user.Smart contact lens 120 can communicate both the captured imageinformation and biometric information to a computing device (not shownin FIG. 1). Smart contact lens 120 can receive input from a computingdevice (not shown in FIG. 1), a user, or dynamic recording program (notshown in FIG. 1), among others. Smart contact lens 120 may communicatevia a wireless network to additional devices (not shown in FIG. 1).

Reference is now made to FIG. 2. FIG. 2 is a functional block diagramillustrating a data processing environment (“environment”), generallydesignated 200, in accordance with an embodiment of the presentinvention. FIG. 2 provides only an illustration of one embodiment anddoes not imply any limitations with regard to the environments in whichdifferent embodiments may be implemented. Many modifications to thedepicted environment may be made by those skilled in the art withoutdeparting from the scope of the invention, as recited by the claims. Inthis exemplary embodiment, environment 200 includes smart contact lens120, remote device 130, server 140, all interconnected over network 150.Remote device 130, and server 140 may include internal and externalhardware components, as depicted and described in further detail withrespect to FIG. 4.

Network 150 may be a local area network (“LAN”), a wide area network(“WAN”), such as the Internet, the public switched telephone network(“PSTN”), a mobile data network (e.g., wireless Internet provided by athird or fourth generation of mobile phone mobile communication), aprivate branch exchange (“PBX”), any combination thereof, or anycombination of connections and protocols that will supportcommunications between smart contact lens 120, remote device 130, andserver 140.

Network 150 architecture may include one or more informationdistribution network(s) of any type(s), such as, cable, fiber,satellite, telephone, cellular, wireless, etc., and as such, may beconfigured to have one or more communication channels. In anotherembodiment, network 150 may represent a “cloud” of computersinterconnected by one or more networks, where network 150 is a computingsystem utilizing clustered computers and components to act as a singlepool of seamless resources when accessed.

The various aspects of network 150 are not limited to radio frequencywireless communications; rather, communication may be accomplished viaany known mediums in the art, including but not limited to, acousticmediums, and optical mediums, such as, visible or infrared light. Forexample, data exchanged between devices, may be transmitted via infrareddata links using well known technologies, such as infrared transceiversincluded in some mobile device models.

Network 150 may include two or more distinct networks. In an embodiment,smart contact lens 120 may be in communication with remote device 130through, for example, a wireless personal area network (“WPAN”), aprivate network carried over wireless network technologies such asBluetooth® or peer-to-peer communications over a wireless LAN (Note: theterm “Bluetooth” is a registered trademark of Bluetooth SIG, Inc. andmay be subject to trademark rights in various jurisdictions throughoutthe world and are used here only in reference to the products orservices properly denominated by the marks to the extent that suchtrademark rights may exist). Networks with a small geographic scope mayrange from Near Field Communication (NFC) to Local Area Networks (LANs).A computer network with a small geographic scope typically does not havea connection to the Internet or other remote networks. Secondly, remotedevice 130 may be in communication with server 140 through, for example,a wide area network (“WAN”), such as the Internet. Additionally, in anembodiment, smart contact lens 120 may be in direct communication withserver 140, and may not be in direct communication with remote device130. For instance, smart contact lens 120 may only have tangentialcommunication with remote device 130, as server 140 communicatesdirectly with smart contact lens 120 and remote device 130.

Smart contact lens 120 is capable of capturing video and transmit theimages to a repository (e.g., information repository 134 and/orinformation repository 144). Additionally, smart contact lens 120 mayinclude internal and external hardware components, as depicted anddescribed in further detail with respect to FIG. 4. Further, smartcontact lens 120 may include a power source (not depicted in FIG. 2). Inthis embodiment, smart contact lens 120 can be powered by the kineticenergy generated by the blinking of a user wearing contact lens 120.Smart contact lens 120 includes sensor 122, and instrument 124. Inanother embodiment, smart contact lens may include dynamic recordingprogram 142 and/or information repository 144.

For illustrative purposes, FIG. 2 depicts only a single sensor 122 and asingle instrument 124, however, it should be understood that a smartcontact lens 120 may include two or more sensors and two or moreinstruments.

In an embodiment, sensor 122 may measure the size of the pupil. Inanother embodiment, sensor 122 may track changes to the size of thepupil. For example, a person's pupil may change size for numerousreasons, one of which is excitement and/or interest in the subjectmatter the user is looking at. In an embodiment of the presentinvention, sensor 122 may detect changes in and measure the size ofpupil 114, in determining when to, and when not to record (seeinstrument 124, for further details). Therefore, based on a level ofinterest, and on a set of detected patterns, behavioral deviations,biometric data, rules, and learned rules, dynamic recording program 142may record what user is looking at.

In an embodiment sensor 122 may measure ambient light. Dynamic recordingprogram may correlate a sudden change in ambient light with a change inpupil dilation to determine recordation is not required as the eye isreacting to the user going into a dark or brightly lit area.Additionally, based on the amount of ambient light the recording device,of instrument 124 (see, instrument 124 description below), is expose theimage correctly and able to thereby capture a crisp video of thesubject, the user is looking at.

In an embodiment, sensor 122 can measure a one or more biological and/orphysiological activities of the user. For example, sensor 122 maymeasure the user's (i) heart rate; (ii) blood pressure; (iii) salinityof the eye; (iv) hormones; (v) temperature; (vi) rate of blinking theeye lid; and (vii) electrical nerve synapsis.

In an embodiment, sensor 122 may measure distance between the user andthe object the user is looking at. Based on the distance, the lens onthe recording device (see, instrument 124 description below), is able tofocus on, and thereby able to thereby capture a video of the subject,the user is looking at.

In an embodiment, sensor 122 may detect the directional movement of theeye. For example, sensor 122 can detect movement of eye as well as otherbiometric data. As discussed in greater detail below, dynamic recordingprogram 142 may utilize the biometric and movement data of the eye toinfer excitement and subsequent commence recording.

In an embodiment, sensor 122 may detect movement of the eye lid (i.e.,the eye lid covering and uncovering the eye, blinking).

In an embodiment, sensor 122 may possess global positioning data therebyproviding dynamic recording program 142 to geo-stamp the location ofeach recorded video.

Instrument 124 may represent any number of instruments used to captureimage (e.g., camera). In an embodiment, instrument 124 may contain aplurality of features allowing it to be embedded in smart contact lens120. For example, instrument 124 may represent one or more videocapturing device which can wireless stream the images to an informationrepository (e.g., information repository 134 and/or informationrepository 144). A video capturing device capable of recording video mayinclude a camera, an optical image capturing device, an infraredcapturing device, a spectral or multi-spectral device, a sonic device,or any other image capturing and/or producing device. In an embodiment,a camera may be capable of taking: (i) moving video images; (ii) stillpictures; and (iii) continuous video images. In an embodiment,instrument 124 may be a camera with a wide angle lens capable of viewingmore than a typical human eye can preserve. In an alternativeembodiment, instrument 124 may be a camera with a tele-photo lenscapable of viewing large distances typical human eye can preserve. In anembodiment the orientation of instrument 124 may be fixed to a positionon smart contact lens 120.

Instrument 124 may represent a wireless transmitter which is capable ofcommunicating through a channel information received from sensor 122and/or images received from a video recorder through wirelesstransmitter of instrument 124.

Environment 200, depicts only a single remote device 130, however, itshould be understood that environment 200 may include two or more remotedevices may be utilized. For example, remote device 130 may be twoseparate devices e.g., a smart phone, and a smart watch. Remote device130 may include a wearable device, and/or a non-wearable device.Wearable device may include, for example, headphones, a smart watch, asmart ring, or any other device that can be worn. Remote device 130 mayinclude internal and external hardware components, as depicted anddescribed in further detail with respect to FIG. 4. As depicted,environment 100 contains remote device 130 includes, sensor 132 andinformation repository 134.

In an embodiment, remote device 130 may be a mobile device such as apersonal digital assistant (PDA), a smart phone, a personal laptopcomputer, a tablet computer, a netbook computer, a personal computer(PC), a desktop computer, or any programmable electronic device capableof communicating with smart contact lens 120, and server 140 via network150. Remote device 130 may have a graphical user interface (GUI)allowing user to manually enable and disable video capture through smartcontact lens 120. The GUI may also allow user to select preferences andprovide configuration and preferences as to when dynamic recordingprogram 142 should engage and/or disengage image capturing instrument ofinstrument 124.

In an embodiment, remote device 130 may, through sensor 132 detect andtrack its geographical location. In an embodiment remote device 130 maythrough sensor 132 may detect and measure the heart rate of user.

Regardless of the type of remote device(s) 130 utilized in environment200, information repository 134 stores information received from sensor132. In other embodiments, information repository 144 may storeinformation from sensor 122 and sensor 132. In this embodiment,information repository 134 may include any suitable volatile ornon-volatile computer readable storage media, and may include randomaccess memory (RAM) and cache memory (not depicted in FIG. 2).Alternatively, or in addition to a magnetic hard disk drive, thepersistent storage component can include a solid state hard drive, asemiconductor storage device, read-only memory (ROM), erasableprogrammable read-only memory (EPROM), flash memory, or any othercomputer readable storage media that is capable of storing programinstructions or digital information. While information repository 134 isdepicted on remote device 130, it is noted that information repository134 directly stream received information to information repository 144on server 140 via network 150. Information repository 134 may beimplemented using any architecture known in the art such as, forexample, a relational database, an object-oriented database, and/or oneor more tables. Information repository 134 may store actual datagathered by sensor 122 and/or sensor 132. Information stored ininformation repository 144 may include: various geographical locations,biometric data, captured video and pictures from smart contact lens 120,and the like.

Further, in an embodiment information repository 134 may store capturedimages received from smart contact lens 120. Similarly, informationrepository 134 may track if and when user deletes the captured images.In this scenario, based on one or more learned situations, dynamicrecording program 142, may learn when not to record video based onrepeated deletions of captured video by user.

In the exemplary embodiment, server 140 is a server computer. In otherembodiments, server 140 may be a management server, a web server or anyother electronic device capable of receiving and sending data. Server140 may be desktop computers, laptop computers, specialized computerservers, or any other computer system known in the art. Server 140represents computer systems utilizing clustered computers and componentsto act as a single pool of seamless resources when accessed throughnetwork 150. For example, such embodiments may be used in data center,cloud computing, storage area network (SAN), and network attachedstorage (NAS) applications. In certain embodiments, server 140represents virtual machines. In other embodiments, server 140 may be amanagement server, a web server or any other electronic device capableof receiving and sending data. In another embodiment, server 140 mayrepresent a server computing system utilizing multiple computers as aserver system, such as in a cloud computing environment. In general,server 140 is representative of any electronic devices, or combinationof electronic devices, capable of executing machine-readable programinstructions, as described in greater detail with regard to FIG. 4.Server 140 contains dynamic recording program 142 and informationrepository 144.

In this embodiment, dynamic recording program 142 determines whether tocapture video based, at least in part, on biometric data, geographicallocations, contextual meanings of subject matter being observed, andlearned behavior of a user wearing smart contact lens 120. Dynamicrecording program 142 can then transmit instructions to instrument 124to automatically commence recording of a subject matter and/or event.Dynamic recording program 142 can further identify contextualinformation of the recorded subject matter, and save the recordedsubject matter based on the identified contextual information. Dynamicrecording program 142 may be located as depicted in server 140; howeverin alternative embodiments (not shown) dynamic recording program 142 maybe located on a remote cloud. Dynamic recording program 142 may beembedded and located within smart contact lens 120. Dynamic recordingprogram 142 may be stored on remote device 130, within informationrepository 134. Dynamic recording program 142 may be stored in apersistent storage component (not depicted) for execution and/or accessby one or more processor(s) via one or more memories (for more detailrefer to FIG. 4). While depicted on server 140, in the exemplaryembodiment, dynamic recording program 142 may be on a remote server or a“cloud” of computers interconnected by one or more networks utilizingclustered computers and components to act as a single pool of seamlessresources, accessible to dynamic cleaning program via network 150.

Regardless of where dynamic recording program 142 is located, dynamicrecording program 142 may have access to one or more different featureswhich analyze and categorize captured video as well as determining whento and when not to record.

In an embodiment of the present invention dynamic recording program 142provides a layer of control regarding when smart contact lens 120 shouldrecord a specific activity, as well as where to save the capturedimage(s).

In an embodiment, dynamic recording program 142 may classify content ofa recording based on cognitive contextual analysis of one or morelearned or defined subjects including but not limited to: (i) persons;(ii) objects; (iii) places; and (iv) activities of the user. In thisscenario, dynamic recording program 142 may determine whether to enablesmart contact lens 120 to record and capture the current context. Forexample, dynamic recording program 142 may continually analyze a streamof images received from smart contact lens and upon a specificoccurrence, enable video capture. In another example, dynamic recordingprogram 142 may continually analyze a stream of images received andrecorded from smart contact lens and upon a specific occurrence, disablevideo capture.

In an embodiment, dynamic recording program 142 based on aclassification content of the recorded content (per a cognitivecontextual analysis) may determine whether the recorded subject is to beprivate publicly accessible. For example, dynamic recording program 142may determine whether the stream the recording from smart contact lens120 to an intermediary device (e.g., remote device 130) or directly to aremotely located information repository (e.g., a cloud basedrepository).

Similarly, in another embodiment, dynamic recording program 142 maydefine persons, objects, places, activities, and the like based on aclassification content of the recorded content (per a cognitivecontextual analysis). For example, specific persons, places, objects,and activities may be given specific categories such as (i) restricted;(ii) public; (iii) private. Thus, based on a specific category aspecific persons, places, objects, and activities falls into, dynamicrecording program 142 determine (i) whether to record and capture theactivity; and, (ii), how and where the captured video should be saved inresponse to recording and capturing the activity. For example, if thevideo capture falls under restricted category, it could be limitedviewing just for the user of smart contact lens 120. If the videocaptured falls under a private category, the video may be viewed by aset of predefined and/or learned persons. Similarly, if the videocaptured falls under a public category, the video may be uploaded to,for instance, social media platform for anyone to view.

For example, an embodiment may classify the content of the capturedsubject based on cognitive system contextual analysis based on learnedand/or pre-defined persons, objects, places, and/or activities of theuser. In this embodiment of the present invention, dynamic recordingprogram 142 may be contextually aware based on learned actions andpre-defined rules from previous capture of images/video responsive tohow the user interacted and/or removed the capture video frominformation repository 134 and/or information repository 144.

Furthermore, dynamic recording program 142 may learn what contextualsituations a user manually disables and enables video capture. Forinstance, if user enables and/or disables video capture within apredefined time period of video capture (before and/or after) dynamicrecording program 142 may learn based on one or more contextualsituations, whether to enable or disable video capture in the future. Inanother embodiment, dynamic recording program 142 may learn one or moresituations (e.g., persons places, objects, and activities) that shouldbe give a specific privilege category such as, restricted, public, andprivate. For instance, when dynamic recording program 142 recognizes thecaptured video should be public, dynamic recoding program 142 may uploadand stream the captured video from smart contact lens 120 to anintermediary device (i.e., remote device 130) or directly to a cloudbased repository (i.e., information repository 144). If, for instance,dynamic recording program 142 recognizes the captured video should berestricted, dynamic recoding program 142 may stop recording and/orupload the captured video from smart contact lens 120 directly to user'sprivate information repository.

In another embodiment, dynamic recording program 142 may detect when auser deletes captured recordings, and learn based on those situationsand scenarios when not to record. For example, if the subject mattercontains specific person or activity, dynamic recording program 142 maylearn not to record when user regularly deletes said recording within adetermined time period after video capture.

In another embodiment, dynamic recording program 142 may measure one ormore biometric statistics of user of smart contact lens 120 to gauge anddetermine interest of user in the subject matter. For instance, a user'sinterest is related to a user's attentiveness and/or importance the userpaces on the object. Therefore, based on the user's interest, dynamicrecording program 142 may determine whether the capture and record theevents taking place. For example, dynamic recording program 142 may,based on sensor 122 readings of smart contact lens 120, correlate pupildilation to determine interest. For instance, dynamic recording program142 identifies a user's interest in an object when the user's pupilsdilate, based on received information from sensor 122. In anotherexample, dynamic recording program 142 may, based on sensor 132 ofremote device 130, correlate heart rate to determine interest. Forinstance, dynamic recording program 142 identifies a user's interest inan object when the user's heart rate increases.

In both aforementioned instances (measuring pupil diameter size andheart rate) dynamic recording program 142 may have one or more systemsin place to identify if the user is interested in the object. Forexample, a typical persons pupils will dilate when user leaves a darkarea and enters a location full of light. Therefore, an embodiment ofthe present invention, sensor 122 be a light detection sensor whichdetects sudden changes in ambient light. Thus, when the user leaves adark area, and enters an area full of bright light, light detectingsensor (part of sensor 122), may recognize the change in ambient lightand dynamic recording program 142 may determine not to capture videoregardless of pupil dilation.

In another example, a typical person's heart rate will increase duringstrenuous activity, e.g. working out. Therefore, an embodiment of thepresent invention, sensor 122 may be a pedometer which detects when theuser is running based on the users pace. Thus when a user starts on ajog, pedometer (part of sensor 122), may recognize the change in theusers pace and then dynamic recording program 142 may determine not tocapture video regardless of the user's increased heart rate.

In addition to learning when to commence and end recording, anembodiment of the present invention may learn when certain objects,situations are not to be captured. In this scenario, such objects and orstations may be pre-defined, and learned.

In addition to learning when to and when not to record, an embodiment ofthe present invention may learn to classify certain captured andrecorded video. Therefore, based on the classification, dynamicrecording program 142 may categorize captured video/images as in aplurality of different predefined categories, for example, (i)restricted, (ii) public and (iii) private. Thus, once the captured videois classified into a category, depending on the category, the capturedvideo may be stored and subsequently shared on different platforms(e.g., social media, public cloud, private network, etc.). Specifically,embodiments of the present invention are contextually aware of specificpersons, places and user activity through facial recognition, objectrecognition, location recognition global positioning systems, biometricsensors, and audio data. Based on the received and derived data, dynamicrecording program 142 identifies specific people, objects, activities,and topics (both oral and written) which are deemed restricted.Conversely, based on the received and derived data, dynamic recordingprogram 142, identifies when to capture specific people, objectsactivities and topics (both oral and written). Specifically, anembodiment of the present invention is contextually aware based onlearned actions and pre-defined rules from previous capture ofimages/video responsive to how the user interacted and/or removed thecapture video from information repository 134 and/or informationrepository 144.

Information repository 144 stores information received from sensor 122and sensor 132. In other embodiments, information repository 144 maystore information from one or more other components of environment 200.In this embodiment, information repository 144 may include any suitablevolatile or non-volatile computer readable storage media, and mayinclude random access memory (RAM) and cache memory (not depicted inFIG. 2). Alternatively, or in addition to a magnetic hard disk drive,the persistent storage component can include a solid state hard drive, asemiconductor storage device, read-only memory (ROM), erasableprogrammable read-only memory (EPROM), flash memory, or any othercomputer readable storage media that is capable of storing programinstructions or digital information. While information repository 144 isdepicted on server 140 it is noted that information repository 144, maybe on a remote server or a “cloud” of computers interconnected by one ormore networks utilizing clustered computers and components to act as asingle pool of seamless resources, accessible to dynamic recordingprogram 142 via network 150. Information repository 144 may beimplemented using any architecture known in the art such as, forexample, a relational database, an object-oriented database, and/or oneor more tables. Information repository 144 stores actual, modeled,predicted, or otherwise derived patterns of user's video recordinghabits as well as biological data based on received sensor data. Forexample, information repository 144 may store all information receivedfrom sensor 122 and/or sensor 132. Information repository 144 maycontain lookup tables, databases, charts, graphs, functions, equations,and the like that dynamic recording program 142 may access to bothmaintain a specific parameter as well as manipulate various parameters.Information stored in information repository 144 may include: variousgeographical locations, specific actions linked to a various detectedissue, a set of rules, learned patterns, biometric data, contextualdata, historical data (e.g., if user deleted video after recording, andif user manually recorded video) and the like.

Information repository 144 may contain information which dynamicrecording program 142 may leverage in determining specific actions toperform based on a variety of sensor 122 and sensor 132 readings.Similarly, information repository may contain various environmentalfactors which may be utilized by dynamic recording program 142 indetermining one or more instances to record and the method ofrecordation saving. Information repository 144 may contain historic datafrom sensor readings and from previous determinations thereby providingdynamic recording program 142 any requisite information to predictwhether user requires recording.

In an embodiment, information repository 144 may contain sensor 122and/or sensor 132 readings in order to forecast and predict whether torecord. Dynamic recording program 142 may cross reference informationcontained in information repository 144 and information repository 134to derive a cognitive pattern, thereby creating new rules and when toenable and disable recording from smart contact lens 120.

Reference is now made to FIG. 3. FIG. 3 is flow chart 300, illustratingoperational steps for contextual video capture, in accordance with anembodiment of the present invention.

In step 310, dynamic recording program 142 receives identifyinginformation. In an embodiment, identifying information may be biometricdata of user. In another embodiment, identifying information may bebased on detected contextual identifier. In another embodiment,identifying information may be both, biometric data and detectedcontextual identifier.

Dynamic recording program 142 may receive identifying information of oneor more contextual identifiers. A contextual identifier is a person,place, object, and/or activity which dynamic recording program 142 canrecognize through facial recognition, object recognition, audiodetection, and location positioning services (e.g., G.P.S.). Contextualidentifiers may be predefined or machined learned. Predefined contextualidentifiers are predefined by the user and or a system administrator.Additionally, predefined contextual identifiers may outline specificcategories to which dynamic recording program 142 is to classify eachcontextual identifier, for example whether the given category is publicor private (see step 330). Machine learned contextual identifiers learnbased on repeated stations whether the user either manually disablesvideo capture, or deletes a previously captured video within a specifictime period (e.g., immediately following capture, or shortly after userrealizes video was captured.) Machine learned contextual identifierslearn may learn to enable/disable video recording based on throughfacial recognition, object recognition, audio detection, and locationpositioning services (e.g., G.P.S.). Further, in an embodiment,contextual identifiers may be received from sensor 122. In anotherembodiment, contextual identifiers may be received from any component ofenvironment 200 (i.e., information repository 134, or informationrepository 144).

Dynamic recording program 142 may receive identifying biometric data ofuser. For example, dynamic recording program 142 may receive pupil sizeand/or detect sudden changes in pupil diameter. In this scenario,dynamic recording program 142 may also receive ambient light readings inaddition to pupil size. In another example, dynamic recording program142 may receive heart rate and/or heart rhythm of user. In thisscenario, dynamic recording program 142 may also receive an indicationof user's activity level such as accelerometers, pedometers, etc. Inanother example, dynamic recording program 142 may detect increaselevels of hormones within user. In this scenario, dynamic recordingprogram 142 may detect increases of adrenalin, oxytocin, etc.

In decision 320, dynamic recording program 142 determines whether tocapture video. Based on biometric data, dynamic recording program 142may determine to record video. In an embodiment, based on contextualidentifiers, dynamic recording program 142 may determine to recordimage. Conversely, based on contextual identifiers, dynamic recordingprogram 142 may determine not to capture image.

In an embodiment, dynamic recording program 142 may determine whether tocapture video based solely on biometric data determine whether to enablevideo capture. In this embodiment, dynamic recording program 142compares received on-demand biometric data to one or more baselinemeasurements in order to determine interest in the subject.

In instances where, dynamic recording program 142 fails to detect anybiometric change and/or a contextual identifier, dynamic recordingprogram 142 will continue to receive identifying information per step310. In instances when identifying information (received in step 310)identify particular biometric data of user and/or specific detectedcontextual identifier, dynamic recording program 142 may determine it isnot appropriate to record. The determination not to record may be basedon a particular learned and/or predefined biometric data response,and/or contextual identifier. For example, through machine learningand/or predefined rules, dynamic recording program 142 may, not recordbased upon detection of a particular hormone via sensor 122, isidentified. In another example, dynamic recording program 142 may notrecord if a light sensor (sensor 122) detects a sudden change in ambienteven if sensor 122 detects a pupil dilation light. In another example,dynamic recording program 142 may not record if a particular person isidentified through facial recognition. In another example, dynamicrecording program 142 may not record if a user is in a particularlocation through, as identified by location determining services (e.g.,GPS, IP address, etc.). In another example, dynamic recording program142 may not record if a particular phrase is identified through audiorecognition. In another example, dynamic recording program 142 may notrecord if a particular object is identified through object recognition.In another example, dynamic recording program 142 may not record if aparticular written phrase is identified through object recognition(e.g., the phrase “confidential”). Therefore, upon determining not torecord, dynamic recording program 142 continues to receive identifyinginformation per step 310, until identifying information such asbiometric data and/or a contextual identifier is detected.

In instances when identifying information (received in step 310)identify particular biometric data of user and/or specific detectedcontextual identifier, dynamic recording program 142 may determine it isappropriate to record. For example, pupil dilation may have a normal andan excited range. The more excited dynamic recording program 142determines the user is, the more likely video capture will be enabled.For example, where biometric data indicates an elevated heart rate,dynamic recording program 142 determines to captures video. For anotherexample, where biometric data indicates a change of pupil dilation(without a change in ambient light), dynamic recording program 142determines to captures video. For another example, where biometric dataindicates a change of pupil dilation (regardless of a change in ambientlight), dynamic recording program 142 determines to captures video. Foranother example, upon detecting pupil dilation (due to decrease ambientlight) and an increased heart rate at bed time (typically users heartrate decreases at bed time), dynamic recording program 142, maydetermine to record as the increased heart rate breaks from users normalroutine. For another example, where biometric data indicates acombination of elevated heart rate and large pupil dilation, dynamicrecording program 142 determines to captures video. For another example,where contextual identifiers such as a location (e.g., conference room)indicate that it is appropriate to record, dynamic recording program 142determines to captures video. In another example, dynamic recordingprogram 142 may determine to record if a user is in a particularlocation through, as identified by location determining services (e.g.,GPS, IP address, etc.). In another example, dynamic recording program142 may record if a particular phrase is identified through audiorecognition. In another example, dynamic recording program 142 mayrecord if a particular object is identified through object recognition.In another example, dynamic recording program 142 may record if aparticular written phrase is identified through object recognition(e.g., the phrase “non-confidential”).

Further, heart rate measurements may be normalized based on changes inuser's level of activity. Therefore, if dynamic recording program 142determines that user's increased heart rate is due to user beingcurrently active, then it is less likely video capture will be enabled.Conversely, if dynamic recording program 142 determines that user'sincreased heart rate is due to user being excited about the subject,then video capture will be enabled.

For another example, specific facial expressions trigger and/or prohibitvideo capture. For example, sensor 122 of smart contact lens 120 maydetect facial movements of user. Dynamic recording program 142, mayanalyze the detected facial movements of user to derive interest in thesubject. Therefore, as the detected facial expresses portray moreexcitement, dynamic recording program 142 will be enabled video capture.Conversely, the less excited the user is about a subject, dynamicrecording program 142 will disable video capture.

Dynamic recording program 142 may receive additional types biometricdata, known in the art and interpret user's excitement and/or interestin the subject.

In an embodiment, dynamic recording program 142 determines to capturevideo by correlating two or more received biometric information toanalyze user's stimulation and interest in the subject. For example, ifpupil dilation depicts an average interest and heart rate portrays a lowinterest, then dynamic recording program 142 may determine not torecord. However, if both heart rate and pupil dilation depict an averageinterest, then dynamic recording program 142 may determine to capturevideo.

Therefore, in decision 320, dynamic recording program 142 determines tocapture video by mapping or correlating interest level and comparing themapped/correlated interest level to a predefined threshold. Therefore,if user's interest is below the threshold, then dynamic recordingprogram 142 may determine not to capture video. If user's interest isabove the threshold, then video capture is enabled.

Further, in decision 320, in an embodiment, dynamic recording program142 may, based solely on contextual identifying data, determine whetherto enable video capture. In this embodiment, dynamic recording program142 identifies the presence of one or more specific identifiers.Specific identifiers may include a specific person, a specific object,user's location, and user's current activity. Once a specific identifieris detected, dynamic recording program 142 may determine whether tocapture video or prohibit video capture.

Received identifying information may be gathered from, audio sources,facial recognition, voice analysis, ocular movements, changes inbiometrics (i.e., pupil size, heart rate, hormone changes, temperaturechanges, etc.), and/or any other analytics for analyzing video/audiodata received from one or more sources, by dynamic recording program142.

In an embodiment, dynamic recording program 142 continually parsesimages received from smart contact lens 120 in order to detect one ormore contextual identifiers. Dynamic recording program 142 can thenutilized the detected contextual identifiers to determine to capturevideo. In an embodiment, dynamic recording program 142 may alsocontinually parses received audio from remote device 130 in order todetect one or more contextual identifiers. Contextual identifiers may bepredefined and/or learned. Predefined contextual identifiers may bebased on facial recognition, object recognition, audio detectionrecognition, and location positioning services (e.g., G.P.S.).Additionally, contextual identifiers may continually change as dynamicrecording program 142 is continually learning. For example, dynamicrecording program 142 may through machine learned contextual identifiersdiscover based on repeated stations whether the user either manuallydisables video capture, or deletes a previously captured video within aspecific time period (e.g., immediately following capture, or shortlyafter user realizes video was captured.) Machine learned contextualidentifiers may learn to enable/disable video recording based on throughfacial recognition, object recognition, audio detection recognition, andlocation positioning services (e.g., G.P.S.).

For example, based on learned contextual identifiers, dynamic recordingprogram 142 may learn to enable/disable video capture based on thepresence of a specific person to be captured based on facialrecognition. For instance, user may delete captured video of a specificperson (e.g., users child), and therefore dynamic recording program 142learns not to capture video of the specific person. Conversely, if userrarely deletes images of a specific person, dynamic recording program142 may determine to always capture video of said person.

For another example, based on learned contextual identifiers, dynamicrecording program 142 may learn to enable/disable video capture based onthe presence of a specific object to be captured based on objectrecognition. For instance, user may delete captured video of food, andtherefore dynamic recording program 142 learns not to capture video foodwhen the user is eating. Conversely, if user rarely deletes images of afood on a plate when eating, dynamic recording program 142 may determineto always capture video of said action. For another example, dynamicrecording program 142 may learn to disable video capture based on thepresence of specific written words. For instance, if user reviews adocument which states the word “confidential” (or the like), dynamicrecording program 142 may disable and delete the captured video.

For another example, based on learned contextual identifiers, dynamicrecording program 142 may learn to enable/disable video capture based onuser located within a specific geographical position based on GPSlocation. For instance, a user may delete captured video of the user'soffice location, and therefore dynamic recording program 142 learns notto capture video food when the user is at work. Conversely, if userrarely deletes images while at work, dynamic recording program 142 maydetermine to always capture video of said action. Additionally,contextual identifiers may be predefined based on the user's location.For instance, when user is at work, (as determined by e.g., GPS locationservices, IP address of remote device, etc.) a user may predefine thelocation to be restricted and prohibit any video capture. Similarly arestricted category may be established when dynamic recording program142, through object recognition, detects a confidentialityidentification on a document.

For another example, based on learned contextual identifiers, dynamicrecording program 142 may learn to enable/disable video capture based onuser current activity as detected based on one or more biometric sensorsand location determining sensors (e.g., GPS). For instance, user maydelete captured video of working out, and therefore dynamic recordingprogram 142 learns not to capture video when user is working out.Conversely, if user rarely deletes images while working out, dynamicrecording program 142 may determine to always capture video when userworks out.

Therefore, in decision 320, dynamic recording program 142 may determinewhether to enable video capture based detecting one or more contextualidentifiers. Therefore, upon detecting a predefined and/or learnedcontextual identifier, dynamic recording program 142 may determinewhether to enable video capture or disable video capture.

Dynamic recording program 142 may analyze both contextual identifiersand biometric data simultaneously to determine whether to capture videoin decision 320. For example, certain contextual identifiers mayindicate the subject to be videotaped is restricted and prevent videocapture, regardless of user's interest level (as derived from receivedbiometric data). In another example, certain contextual identifiers mayindicate the subject to be videotaped is always to be recorded and toenable video capture, regardless of user's interest level (as derivedfrom received biometric data).

Therefore, in decision 320, dynamic recording program 142 may determinewhether to enable video capture based detecting one or more contextualidentifiers regardless of biometric data (even when biometric dataindicates no level of interest). Similarly, dynamic recording program142 may determine to restrict and prohibit video capture based on one ormore contextual identifiers regardless of biometric data (even whenbiometric data indicates a level of interest). Further, dynamicrecording program 142 may determine whether to enable video capturebased determined interest, through biometric data, regardless ofdetecting one or more contextual identifiers. Therefore, dynamicrecording program 142 may determine whether to enable video capture ordisable video capture, based upon detecting a predefined and/or learnedcontextual identifier coupled with an indication of interest based onreceived biometric data.

If in decision 320, dynamic recording program 142 determines to capturevideo then in step 330, dynamic recording program 142 identifies thevideo capture category. In an embodiment, video capture category may bepublic or private. A public category is a category which may be socialmedia and/or other medium which is accessible to the public. A privatecategory limits access to the captured images only to the user.Additional categories may be present in other embodiments. For example,an additional categories may be limited to one or more social group. Forinstance, if a specific person is detected in the image, then thecategory may be semi private, allowing access to a predefined set ofpersons.

In an embodiment, after determining to capture video (see decision 320),dynamic recording program 142 determines where the captured video shouldbe streamed and/or saved based on identifying the video capture category(step 330). For example, dynamic recording program 142 may continuallyanalyze images from smart contact lens 120, in order to detect anycontextual identifiers, in order to place the captured image into aspecific category. In another example, dynamic recording program 142 mayat predetermined time intervals analyze images from smart contact lens120, in order to detect any contextual identifiers, in order to placethe captured image into a specific category. In another example, dynamicrecording program 142 may on demand analyze images from smart contactlens 120, in order to: (i) detect any contextual identifiers; and (ii)place the captured image into a specific category.

Dynamic recording program 142 may have predefined and/or learnedcontextual situations which indicate which category the captured videoshould be classified. In an embodiment, based on the identifiedcontextual situation, the captured video may be classified as public,private, etc. For example, users may predefine any captured video to beprivate upon identifying a specific person (i.e., user's child) throughfacial recognition, in the captured video. For another example, user maypredefine any captured video to be public upon identifying a specificobject (i.e., user's vehicle) through object recognition, in thecaptured video. For another example, user may predefine any capturedvideo to be private upon identifying user is in a specific location(i.e., user's bedroom) through object location based services. Foranother example, users may predefine any captured video to be publicupon identifying user is on vacation touring a known landmark (i.e.,user's bedroom) through object location based services. For anotherexample, users may predefine any captured video to be public uponidentifying a predefined string of spoken words (i.e., a specificphrase) through audio recognition, associated with the captured video.

In step 340 dynamic recording program 142 records and saves the video,based on the identified category (see step 330). In this embodiment, ininstances where dynamic recording program 142 identifies the event asprivate, dynamic recording program 142 records and saves the video bytransmitting a set of instructions to instrument 124 of smart contactlens 120 to record and save the video to information repository 144. Ininstances where dynamic recording program 142 identifies the event aspublic, dynamic recording program 142 can transmit a set of instructionsto instrument 124 to stream to information repository 134. In anembodiment, the captured video may be streamed to information repository134 on remote device 130. In another embodiment, the captured video maybe streamed to information repository 134 on server 140. In anotherembodiment, the captured video may be streamed directly to social media.In another embodiment, the captured video may be streamed to user'sprivate information repository. In another embodiment, the capturedvideo may be streamed to a shared repository.

FIG. 4 is a block diagram of internal and external components of acomputer system 400, which is representative of the computer systems ofFIG. 1, in accordance with an embodiment of the present invention. Itshould be appreciated that FIG. 4 provides only an illustration of oneimplementation, and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environment may be made.

Computer system 400 includes communications fabric 402, which providescommunications between computer processor(s) 404, memory 406, persistentstorage 408, communications unit 412, and input/output (I/O)interface(s) 414. Communications fabric 402 can be implemented with anyarchitecture designed for passing data and/or control informationbetween processors (such as microprocessors, communications and networkprocessors, etc.), system memory, peripheral devices, and any otherhardware components within a system. For example, communications fabric402 can be implemented with one or more buses.

Memory 406 and persistent storage 408 are computer readable storagemedia. In this embodiment, memory 406 includes random access memory(RAM) 416 and cache memory 418. In general, memory 406 can include anysuitable volatile or non-volatile computer readable storage media.

Persistent storage 408 may include, for example, a plurality of magnetichard disk drives. Programs are stored in persistent storage 408 forexecution and/or access by one or more of the respective computerprocessors 404 via one or more memories of memory 406. In thisembodiment, persistent storage 408 includes a magnetic hard disk drive.Alternatively, or in addition to a magnetic hard disk drive, persistentstorage 408 can include a solid state hard drive, a semiconductorstorage device, read-only memory (ROM), erasable programmable read-onlymemory (EPROM), flash memory, or any other computer readable storagemedia that is capable of storing program instructions or digitalinformation.

The media used by persistent storage 408 may also be removable. Forexample, a removable hard drive may be used for persistent storage 408.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer readable storage medium that is also part of persistent storage408.

Communications unit 412, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 412 includes one or more network interface cards.Communications unit 412 may provide communications through the use ofeither or both physical and wireless communications links. Software anddata used to practice embodiments of the present invention can bedownloaded to computer system 400 through communications unit 412 (i.e.,via the Internet, a local area network, or other wide area network).From communications unit 412, the software and data may be loaded topersistent storage 408.

I/O interface(s) 414 allows for input and output of data with otherdevices that may be connected to computer system 400. For example, I/Ointerface 414 may provide a connection to external devices 420, such asa keyboard, keypad, a touch screen, and/or some other suitable inputdevice. External devices 420 can also include portable computer readablestorage media such as, for example, thumb drives, portable optical ormagnetic disks, and memory cards. Software and data used to practiceembodiments of the present invention, can be stored on such portablecomputer readable storage media and can be loaded onto persistentstorage 408 via I/O interface(s) 414. I/O interface(s) 414 also connectto a display 422.

Display 422 provides a mechanism to display data to a user and may be,for example, a computer monitor. Display 422 can also be an incorporateddisplay and may function as a touch screen, such as a built-in displayof a tablet computer.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The terminology used herein was chosen to best explain the principles ofthe embodiment, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A method comprising: receiving, by one or moreprocessors, a set of identifying information and a video stream from acontact lens; determining, by one or more processors, whether to capturethe video stream, wherein determining whether to capture the videostream is based on, at least one of, the user interest level exceeding athreshold and detection of a contextual identifier within the receivedvideo stream from the contact lens; classifying, by one or moreprocessors, the video stream into a category; responsive to determiningto capture the video stream, saving, by one or more processors, thevideo stream based on the classification category of the video stream;and creating, by one or more processors, a new contextual identifier,wherein creating a new contextual identifier comprises: identifying, byone or more processors, at least two incidences of the user deleting thesaved video stream, subsequent to the saving the video stream, within apredetermined time, deriving, by one or more processors, at least onecommon person, object, place and activity within each of the identifiedincidences, and responsive to deriving at least one common person,object, place and activity within each of the identified incidences,determining, by one or more processors, a habitual pattern of the userdeleting the saved video stream, subsequent to the saving of the videostream.
 2. The method of claim 1, wherein the contextual identifier isone or more predefined person, object, place and activity.
 3. The methodof claim 1, further comprises: determining, by one or more processors,the user interest level, based on biometric data of the user, wherein:the biometric data of the user is received from the set of identifyinginformation, and determining the user interest level comprisesdetecting, by one or more processors, at least one of: a change in theuser's pupil dilation; a change in the user's heart rate; and a changein the user's facial expression.
 4. The method of claim 1, whereindetecting the contextual identifier comprises: analyzing, by one or moreprocessors, the received video stream from the contact lens; andapplying, by one or more processors, at least one audio/videorecognition system, wherein the at least one audio/video recognitionsystem searches the received video stream for a predefined person, apredefined object, a predefined place, a predefined activity, and apredefined audible clip.
 5. The method of claim 1, wherein classifyingthe video stream into a category, further comprises: establishing, byone or more processors, at least two categories, wherein a firstcategory is public and the second category is private; and determining,by one or more processors, a pattern wherein the pattern is: of the userplacing the video stream into one of the at least two categories, andbased on deriving, by one or more processors, at least one commonperson, object, place and activity within each category.
 6. The methodof claim 1, further comprising: determining, by one or more processors,not to capture the video stream, wherein determining not to capture thevideo stream is based on, at least one of, the user interest level belowa threshold and detecting a second contextual identifier within thereceived the video stream from a contact lens, wherein the secondcontextual identifier is classified as restricted.
 7. A computer programproduct comprising: a computer readable storage medium and programinstructions stored on the computer readable storage medium, the programinstructions comprising: program instructions to receive a set ofidentifying information and a video stream from a contact lens; programinstructions to determine whether to capture the video stream, whereinthe program instructions to determine whether to capture the videostream is based on, at least one of, the user interest level exceeding athreshold and detection of a contextual identifier within the receivedvideo stream from the contact lens; program instructions to classify thevideo stream into a category; responsive to determining to capture thevideo stream, program instructions to save the video stream based on theclassification category of the video stream; and program instructions tocreate a new contextual identifier, where the program instructions tocreate a new contextual identifier comprise: program instructions toidentify at least two incidences of the user deleting the saved videostream, subsequent to the saving the video stream, within apredetermined time, program instructions to derive at least one commonperson, object, place and activity within each of the identifiedincidences, and responsive to deriving at least one common person,object, place and activity within each of the identified incidences,program instructions to determine a habitual pattern of the userdeleting the saved video stream, subsequent to the saving of the videostream.
 8. The computer program product of claim 7, wherein thecontextual identifier is one or more predefined person, object, placeand activity.
 9. The computer program product of claim 7, furthercomprising: program instructions to determine the user interest level,based on biometric data of the user, wherein: the biometric data of theuser is received from the set of identifying information, and theprogram instructions to determining the user interest level comprise:program instructions to detect at least one of: a change in the user'spupil dilation; a change in the user's heart rate; and a change in theuser's facial expression.
 10. The computer program product of claim 7,wherein the program instructions to detect the contextual identifiercomprise: program instructions to analyze the received video stream fromthe contact lens; and program instructions to apply at least oneaudio/video recognition system, wherein the at least one audio/videorecognition system searches the received video stream for a predefinedperson, a predefined object, a predefined place, a predefined activity,and a predefined audible clip.
 11. The computer program product of claim7, wherein the program instructions to classify the video stream into acategory, further comprise: program instructions to establish at leasttwo categories, wherein a first category is public and the secondcategory is private; and program instructions to determine a patternwherein the pattern is: of the user placing the video stream into one ofthe at least two categories, and based on program instructions to deriveat least one common person, object, place and activity within eachcategory.
 12. The computer program product of claim 7, furthercomprising: program instructions to determine not to capture the videostream, wherein determining not to capture the video stream is based on,at least one of, the user interest level below a threshold and detectinga second contextual identifier within the received the video stream froma contact lens, wherein the second contextual identifier is classifiedas restricted.
 13. A computer system comprising: one or more computerprocessors; one or more computer readable storage media; programinstructions stored on the one or more computer readable storage mediafor execution by at least one of the one or more processors, the programinstructions comprising: program instructions to receive a set ofidentifying information and a video stream from a contact lens; programinstructions to determine whether to capture the video stream, whereinthe program instructions to determine to capture the video stream isbased on, at least one of, the user interest level exceeding a thresholdand detection of a contextual identifier within the received videostream from the contact lens; program instructions to classify the videostream into a category; responsive to determining to capture the videostream, program instructions to save the video stream based on theclassification category of the video stream; and program instructions tocreate a new contextual identifier, where the program instructions tocreate a new contextual identifier comprise: program instructions toidentify at least two incidences of the user deleting the saved videostream, subsequent to the saving the video stream, within apredetermined time, program instructions to derive at least one commonperson, object, place and activity within each of the identifiedincidences, and responsive to deriving at least one common person,object, place and activity within each of the identified incidences,program instructions to determine a habitual pattern of the userdeleting the saved video stream, subsequent to the saving of the videostream.
 14. The computer system of claim 13, wherein the contextualidentifier is one or more predefined person, object, place and activity.15. The computer system of claim 13, further comprising: programinstructions to determine the user interest level, based on biometricdata of the user, wherein: the biometric data of the user is receivedfrom the set of identifying information, and wherein the programinstructions to determine the user interest level comprise: programinstructions to detect at least one of: a change in the user's pupildilation; a change in the user's heart rate; and a change in the user'sfacial expression.
 16. The computer system of claim 13, wherein theprogram instructions to detect the contextual identifier comprise:program instructions to analyze the received video stream from thecontact lens; program instructions to apply at least one audio/videorecognition system, wherein the at least one audio/video recognitionsystem searches the received video stream for a predefined person, apredefined object, a predefined place, a predefined activity, and apredefined audible clip.
 17. The computer system of claim 13, whereinthe program instructions to classify the video stream into a category,further comprise: program instructions to establish at least twocategories, wherein a first category is public and the second categoryis private; and program instructions to determine a pattern wherein thepattern is: of the user placing the video stream into one of the atleast two categories, and based on program instructions to derive atleast one common person, object, place and activity within eachcategory.